AI-Enabled Analytics for Business

We have methodically walked you through the application of AI and analytics in business and provided the Roadmap to the Analytics Culture for enhanced business performance. While analytics projects have had an abysmal track record, it has been largely due to executives' failure to realize the value of AI and analytics, failure of clarity of vision to a Roadmap to implement analytics, or failure from misalignment/derailment from the Roadmap. These failures are choices that this book has identified and given you the knowledge to correct. As we have repeated, the road to AI-enabled analytics is not long, hard, or expensive—it is simply disciplined!

CHAPTER 1: A Primer on AI-Enabled Analytics for Business AI AND ML—SIMILAR BUT DIFFERENT

MACHINE LEARNING PRIMER

CHAPTER 3: Myths and Misconceptions About Analytics DATA SCIENTIST MISCONCEPTION AND MYTH SHOT IN THE DARK

BASS-ACKWARD

MANUFACTURING AND SUPPLY CHAIN DEMAND PLANNING AND INVENTORY CONCLUSION

NOTES PART II: Roadmap

CHAPTER 5: Roadmap for How to Implement AI-Enabled

CONCLUSION NOTES

CHAPTER 7: Implementing Analytics DEFINE THE PROBLEM

SELECT AN ANALYTICS SOFTWARE POC VENDOR PERFORM THE ANALYTICS POC

BENCHMARK PEOPLE SKILLSET

CHAPTER 8: The Role of Analytics in Strategic Decisions HOW WE TRICK OURSELVES

TACTICS THAT AFFECT STRATEGY

KEY PERFORMANCE INDICATORS (KPIs) AND STRATEGIC OBJECTIVES

THE ANALYTICS SCORECARD™ CONCLUSION

NOTES PART III: Use Cases

CHAPTER 9: Cases of Analytics Failures from Deviation to the

POC RESULTS—REALIZING THE THREE GOALS TEST AND LEARN

ASSESSING ANALYTICS PERSONAS MOVING FORWARD

CHAPTER 12: Use Case: Analytics Are for Everyone THE ROAD TO ANALYTICS

STEPPING INTO ANALYTICS ANALYTICS IS FOR ALL Epilogue

APPENDIX: Analytics Champion Framework: The Fundamental Qualifications, Skills, and Project Steps for the Analytics Champion

ANALYTICS CHAMPION QUALIFICATIONS ANALYTICS CHAMPION SKILLSETS

STARTING AN ANALYTICS PROJECT

Figure 4.1 Benchmark analytics business partner Figure 4.2 Monte Carlo simulation.

Figure 4.3 Fair Challenge.

Figure 4.4 Sales deal path to close assessment Chapter 5

Figure 5.1 From data to insights.

Figure 5.2 Analytics Intelligence, decisions, and Personas Figure 5.3 Informative vs Insightful

Figure 5.4 People proportional soft skills Figure 5.5 Tableau Executive Dashboard (https://www.tableau.com/solutions/it Figure 5.6 Tableau Dashboard

Figure 5.7 Forecast comparisons and reasonability test Figure 5.8 Proportion of people hard-skill utilization in an

Figure 7.1 Five steps to implementing analytics Figure 7.2 Trend and predicted trend direction Chapter 8

Figure 8.1 BSC for PhoneCalls-R-Us Figure 8.2 Analytics Scorecard.

Chapter 10

Figure 10.1 Sales efficiency KPI Figure 10.2 Tequila products report.

Figure 10.3 AI-calculated three-month forecast Figure 10.4 Correlation for the dinner meal period Figure 10.5 Correlation for the late night meal period Chapter 11

Figure 11.1 Spares planning predictive action report Chapter 12

Figure 12.1 Trend of prediction Appendix

Figure A.1 Project Management Principles for the AC Figure A.2 Sample Gantt chart.

Figure A.3 Project status report

Figure A.4 Heat map color explanations.

Figure A.5 Components of strategic leadership Figure A.6 Ready, willing, and able.

Figure A.7 Systematic thinking about a plant Figure A.8 Path of Hollywood stories.

Figure A.9 Story path for management Figure A.10 Analytics project path Figure A.11 Pathways of death.

AI-Enabled Analytics for

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I would like to dedicate this book to my wife Claudia, whose endlesspatience, bright smile, and intelligence have always been a source ofinspiration I also want to acknowledge my parents and brother, who

provided gentle guidance and love I especially want to thank my children,Nicole, Dana, and Jonathan, who inspire and always bring out the best inme.

To Dana, forever in my heart.

Lawrence S MaiselThis book is dedicated in loving memory of my mother, Joy, the merrimentof my grandmother, Tess, and the wisdom and discipline of my grandfather,Ruby The best of life and the greatest of gifts I have are from my sister,Alice, wife, Val, and daughter, Megan.

Robert J ZwerlingThis book was written in memories of my parents, who patiently helped melearn I also want to acknowledge my wife, Anne Without you this wouldnot be possible.

Jesper H Sorensen

We thank Kent Bearden, Jonathan Morgan, and Lisa Tapp for sharing their experiences and helping us learn the ways AI and analytics contribute to improving their operations With gratitude, we also acknowledge the support and editorial assistance of Sheck Cho and Susan Cerra of Wiley, which enabled us to complete this book.

Everywhere you turn, you hear or read about artificial intelligence (AI) and the emerging importance of digital transformation To be competitive in modern business, decision-making needs to evolve into a more objective, insightful, and unbiased process that is powered by the application of AI-enabled analytics.

We have written AI-Enabled Analytics for Business: A Roadmap forBecoming an Analytics Powerhouse for executives to gain a solid

understanding of AI and analytics that will give clarity, vision, and voice to integrating them in business processes that will be impactful and increase business performance.

Today, there is more promise than practice in implementing AI and analytics for data-driven decisions As you will learn, there are twice as many analytics failures than successes, and there are twice as many

successes that are abandoned rather than sustained The good news is that almost all failure can be traced back to executive decisions that are entirely avoidable and easily identified.

Further, AI is not the sole purview of big companies, big data, and big data projects that seek to boil the ocean The butcher, baker, and candlestick maker can all incorporate AI to increase productivity, reduce workforce, retain higher-skilled talent, and enhance the customer's experience In fact, AI and analytics are better done incrementally, building on each success to scale the business to become an analytics powerhouse.

Our research, training, consulting, and on-the-ground experiences with AI-enabled analytics have shaped our perspectives, refined our practices, and tested our tactics We have worked side by side with executives like you, and our empirical results demonstrate the critical factor to success is the executive's mindset to the value of analytics and commitment to allocate the resources to building the Analytics Culture This book gives you the

Roadmap to implement AI and analytics, which, as you will learn, the

executive will make or break As we will show, failure is a choice; the good

news is that it is eminently avoidable, and we have specified the steps for success.

In Part I, we cover the fundamentals of AI and analytics, beginning in Chapter 1 to untangle the many seemingly synonymous terms, partitioning tools that do and do not do analytics, and the ROI of AI It is essential to

know the difference between analysis, which is the application of arithmeticon data to yield information, and analytics, which is the application of

mathematics on data to yield insights In Chapter 2, we illuminate why analytics is essential in business and share Noble Prize-winning research that recognizes the limitations of human decision-making based on biased intuition and gut feel, and why analytics must be included as the essential unbiased component Chapter 3 discusses myths and misconceptions regarding the approach to analytics, and Chapter 4 takes you through

several applications of AI and analytics across different business functions In Part II, we define the Roadmap for how to implement AI-enabled

analytics for data-driven decisions and the contributions of executives for becoming an analytics powerhouse Chapter 5 is the fulcrum of this book and delivers a detailed discussion of analytics as more than a tool—it is a culture with four components: Mindset, People, Processes, and Systems When these components are aligned, immense value to optimize

performance is created, and we delineate in depth how this is accomplished In Chapter 6, you will learn that executive action determines the successful implementation of the Analytics Culture, and you will see what executive actions are needed Further, we introduce the Analytics Champion, who supports the executive and delivers the tactical implementation of the

Analytics Culture In Chapter 7, we specify with clarity and simplicity how to implement analytics and show that achieving it is not time-consuming, hard, or expensive—it is a discipline Chapter 8 links analytics to strategic decisions and debuts the new and innovative Analytics Scorecard, which elevates the traditional and subjective Business Scorecard into a

quantitative cause-and-effect delineation of strategies that can drive increased business performance.

In Part III, we present specific use cases that illustrate key themes and confirm our approach and insights conveyed in earlier chapters As there is more to learn from failure than success, Chapter 9 discusses instances

across several industries where analytics successes became failures Chapter

10 tells the story of a hospitality company's analytics proof of concept that yielded optimized staffing while maintaining excellent customer service, significant cost savings, and opportunities to boost revenue and profit—yet failed because the senior executive did not believe in investing in analytics Chapter 11 is the story of achieving insights that incrementally progress toward a data-driven culture from analytics in demand planning and supply chain Finally, Chapter 12 puts an exclamation point on the notion that AI and analytics are for everyone, not just big companies, through the story of a medium-size art museum and its CFO's curiosity, which led to learning about analytics and discovering how it provides insights.

For your convenience, we have also included an appendix for the Analytics Champion that will guide the executive in selecting the right person and provide the Champion with skillsets and tools needed for implementing the first analytics project and scaling the Analytics Culture.

An executive's job is to manage risk, not avoid it Yet many executives are too risk-averse and choose not to make decisions because the risk of failure blinds them to see the opportunity for success While information is nearly always imperfect, employing AI and analytics gives vision to the future that mitigates risk for better decision-making This book is for you, the

executive and aspiring executive, to arm you with the knowledge to lead your organization to become an analytics powerhouse.

With this introduction, we welcome you to the Undiscovered Country—the future!

PART I

Fundamentals

CHAPTER 1

A Primer on AI-Enabled Analytics forBusiness

Knowledge will forever govern ignorance; and a people who mean to betheir own governors must arm themselves with the power which

knowledge gives.

—James Madison1 Artificial intelligence (AI) dates back over 75 years Alan Turing, a

mathematician, explored the mathematical possibility of AI, suggesting that “humans use available information as well as reason in order to solve

problems and make decisions,” and if this premise is true, then machines can do so too This was the basis of his 1950 paper “Computing Machinery and Intelligence,” in which he discussed “how to build intelligent machines and how to test their intelligence.”2

So, what is artificial intelligence? Very broadly speaking, it is the ability of

a machine to make decisions that are done by humans But what does that mean, what does AI look like, and how will it change our lives and society?

We all know that AI, sooner or later, will be part of all businesses But when

it is part of the business is entirely dependent on what each executive knows and understands about AI and analytics And here lies the chasm between the early adopters and the rest of the pack.

According to Grant Thornton's 21 May 2019 report “The Vital Role of the CFO in Digital Transformation,” the 2019 CFO Survey of Tech Adoption covered several technologies, including advanced analytics and machine learning 38% of respondents indicated that they currently implemented advanced analytics, and 29% are planning implementations in the next 12 months For machine learning technology, the survey results said that 29% had implemented it and 24% were planning to implement in the next 12 months Impressive returns from the survey's sample set, and indicative of the priority of and accelerating trend in the adoption of analytics and AI

throughout business However, while conveying progress in its best light, this survey is a poor showing of a glass that is not even half full.

Implementations of AI are just scratching the surface, as projects have been highly targeted to only certain areas of the business and for certain tasks So, while the movement to incorporate advanced analytics is in the right direction, there are many more failures than successes This is disturbingly bad news, which we shall learn largely rests with executives The good news is that AI and analytics failures are eminently avoidable.

Many executives lack clarity of vision and voice to how they will navigate their business, division, group, or department through the adoption of analytics and AI Other executives think they know what AI enablement means but are often working from poorly defined terms or misconceptions about analytics Their knee-jerk response is to hire consultants and buy AI-enabled analytics software without fully understanding how analytics will be used to drive decisions.

Cries of “We need better forecasting” and “What factors are driving our business?” and “We must get smarter about what we do” echo in

boardrooms and executive conference rooms But how exactly is this done?

Not what, but how? The “what,” many an executive has read from a

mountain of consulting reports; but the “how” is unclear and is why too many businesses are lagging in their adoption of AI and analytics.

In this chapter, we lay the foundation for this book by untangling terms and terminology with definitions and giving a ground-level introduction in select technologies (for the purpose of understanding, not to become experts) We will pursue a high-level discussion of AI, machine learning (ML), and analysis vs analytics, followed by an explanation of business intelligence and data visualization and how these are different from

analytics We will introduce the application of AI-enabled analytics in the context of insights and the contrast between biased vs unbiased predictions Finally, we will position the importance of AI by discussing its ROI.

AI AND ML—SIMILAR BUT DIFFERENT

We see the widely used phrase “AI and ML” and conjure these as linked at the hip; but while related, they are not one and the same First, AI is a

superset, covering all that is considered artificial intelligence The

overarching concept of AI is simply a machine that can make a human decision Any mode of achieving this human decision by a machine is thus

AI, and machine learning is one such mode or subset of AI Therefore, all

ML is AI, but not all AI is ML.

Accordingly, ML is one form of AI ML is a widely used method for

implementing AI, and there are many tools, languages, and techniques available ML engages algorithms (mathematical models) that computers use to perform a specific task without explicit instructions, often relying on patterns and inference, instead.

Another popular form of AI is neural networks that are highly advanced and based on mirroring the synapse structure of the brain So, ML and neural networks are both subsets of AI, as depicted in Figure 1.1, as well as other forms of AI (that is, any other technology/technique that enables a machine to make a human decision).3

Figure 1.1 Superset and subsets of AI.

MACHINE LEARNING PRIMER

This section offers a brief orientation to ML ML is a technique and

technology that today requires specialized skills to use and deploy ML is an AI engine often used with other tools to render the ML output useful for decisions For example, suppose a bank wants to expand the number of loans without increasing the risk profile of its loan portfolio ML can be used to make predictions regarding risk, and then the results are imported to

spreadsheets to report those new additional loan applicants that can now be approved.

Large ML projects often involve the collaboration of data scientists, programmers, database administrators, and application developers (to render a deliverable outcome) Further, ML needs large volumes of high-quality data to “train” the ML model, and it is this data requirement that causes 8 of 10 ML and AI projects to stall.4 While ML is popular and powerful, it is not easy Many new software applications are making ML use easier, but it is still mostly for data scientists.

Before an ML project can begin, its “object” must be defined: that is, what is to be solved For example, suppose we want to predict which customers on our ecommerce website will proceed to check out (vs those who exit before checking out) As presented in Figure 1.2, the process to go from the object to deployed solution has many steps, including collection of data, preparation of data, selecting the algorithm and its programming, model training, model testing, and deployment Any failure at any point will require a reset and/or restart back to any previous point in the process.3

Figure 1.2 ML process.

ML has a limitation in that the solution of the object is highly specific to the data used to train the ML model Most often, the model is not transportable, even to a similar business or a similar department within the same business Also, as mentioned, the use of ML often requires other tools to render its results useful for consumption by business managers However, while complex, ML can offer high business value with a wide range of

applications: for example, predicting customer churn, sales deals that will close in the next 60 days, drugs that are likely to proceed to the next phase in trials, customers who are more likely to buy with a 5% discount, demand forecasting, and so on.

ANALYTICS VS ANALYSIS

Another set of terms to get our arms around is analysis and analytics.

Analysis, in business reporting, involves calculations of arithmetic (add, subtract, multiply, and divide), whereas analytics for business encompasses mathematics (algebra, trigonometry, geometry, calculus, etc.) and statistics (about the study of outcomes).

In a profit and loss statement, there is a variance analysis of current year actual performance against budget The analysis is expressed as the

difference in dollars and as a percent The variance analysis uses arithmetic to make a measurement of the existing condition of the company compared

to what it planned for the year This analysis is comparative information

from arithmetic on data and descriptive of a current situation, but it is not an

insight that is additive to a decision.

Insight, as defined with respect to the value from data, is that not knownabout the business and when known should affect decisions, and insights are

derived from analytics that applies mathematics to data.

For example, say sales are down 15% for the past three months, but sales are predicted to increase this month This prediction is based on a

correlation of unemployment as a three-month inverse leading indicator to sales, meaning as unemployment goes down, sales will go up In this

example, unemployment has been dropping for the past three months, so the prediction is for sales to increase in the current month.

The use of correlations to make a prediction is analytics that reveals an insight, which was not known from the data or information from the analysis of the data, and which when known will affect decisions In this case, without knowing the prediction of the lead indicator, the business would run deep discounts to attract sales However, knowing that sales are predicted to reverse direction would cause the business not to discount or to only offer small discounts.

As such, to crystalize and distinguish the important definitions of insightsand information, we repeat that insights are derived from the application ofmathematics on data, while information is derived from the application ofarithmetic on data Information is used to support a decision, whereasinsights are used to affect a decision.

Accordingly, analytics can powerfully reveal unbiased insights, as it applies mathematics on data that is void of the personal and political pressures that are exerted on humans when they make forecasts and predictions As

humans, we want the future to be what we desire or what we need, so we can make any forecast come to our desired outcome As such, analytics is especially potent to enable unbiased data-driven decisions.

BI AND DATA VISUALIZATION VS.ANALYTICS

Business intelligence (BI) tools date back to the 1980s and enabled multidimensional reporting BI went beyond spreadsheets to ingest large amounts of data from several data sources and then segment (into separate dimensions) the data into hierarchies This approach gave users the ability to organize and dive into more data more intelligently.

Today, legacy BI tools have essentially become data-marts for data

extraction into spreadsheets for reporting BI tools are largely maintained

by IT and require programming to build cubes (specialized BI databases) to

respond to predefined questions However, legacy BI is too rigid and complex for most users, so IT departments often program user-requested reports and data extractions (for download to other applications).

The complexity of BI gave birth to data visualization tools that were

introduced in the 2000s and offered graphic representations of data in many

forms, often combined into dashboards to render a story about key aspects

of the business Dashboards can be informative but typically not analytical.

The reference to data visualization says it all in its name It is visualizing

data, not applying mathematics on data An excerpt from a 2019 report from the Finance Analytics Institute (www.fainstitute.com), “Visualization vs Analytics, what each tool is, how they are different & where they

apply,” offers a clear discussion of visualization:4

Dashboards are of prime value to combine visual charts with tabular data of KPIs and key values for comparisons.

The picture below is … where data and images of trends can work together to offer a view to the past and present Like a car's dashboard, the numerical readings at the top tell key performance data needed to be known; e.g if we're running low on gas…

But dashboards are not predictive, and views of past data can lead to false negatives or positives of the future Look at the image below [Figure A]…

The historical trend is essentially up So, what's the next bar to follow? Up? Down? What decision would you make if you predicted up? What would happen if you guessed wrong? As seen on the chart below [Figure B], the next bar was substantially down.

Figure A

Figure B

Visualization gives colors and images that intrigue the eye But there is pretty and there is practical, and the two should not be confused—although they often are Far too frequently, dashboards become an exercise in art vs business The rendering of a dashboard should be to make better decisions; so when viewing a dashboard, always ask, “Will what I'm seeing help inform me to make a better decision? What decision?” If the answer is not definitive, then the dashboard is art, not business.

We like to say that AI and analytics can torture data until it confesses! The

“confession” obtained from analytics, which applies mathematics on data, can better inform us about the future; and decisions are about the future! Consider, have you ever made a decision about the past? Well, no, other than to say that the decision you made when the past was the future turned out to be a good or bad decision While this bit of time travel may be

confusing, the point is that using tools that display data from the past is only part of the inputs needed to make decisions about the future.

Therefore, it is important to distinguish that data visualization is largely a tool of reporting and displaying past data and information, whereas AI and analytics tools use past data to bring insights that make predictions and forecasts about the future.

For example, returning to the two charts in Figure A and Figure B, the question was what the next bar would be on the trend in Figure A: up or

down? A viewer of the chart might lean to up because the general direction

of the trend is up or due to a personal need/desire to have the trend continue up However, applying the statistical process control index on the data in Figure A would predict the next bar to be materially down—which it was, as depicted in Figure B.

This is a beautiful example of applied statistics to reveal an unbiased insight that can, and should, materially impact a decision Whereas reporting and data visualization informs what happened and where it happened, analytics powerfully advises what will happen and how to make it happen As we shall explore in depth in Chapter 5, using the full range of tools, decisions can be enhanced through information and insights that span a continuum of time in the past, present, and future.

BIASED VS UNBIASED

Most planning, budgeting, and forecasting are biased: that is, a value for the

future that is based on a human's guess While the guess may be from experience or gut feel, it is a value that is not mathematically calculated from past performance of the business Biased forecasts are always fraught with human frailties because, as mentioned, they are about what we want or need the future to be How many times have you made a spreadsheet and not liked the outcome displayed? Hardly ever, for most of us—we simply change the values and, voilà, get what we want Biased decision-making will be explored further in Chapter 2.

Many sales teams pronounce their “forecasts” with immense certitude by claiming the forecast is from the CRM system The importance of the CRM is to establish the credentials of the source, like the Good Housekeeping seal of approval It is authority, credibility, and accuracy all rolled into one.

But—and this is a big but—the forecast is merely the sales rep's guess of

when the deal will close.

A company typically establishes a ranking system for where a sales deal is in the pipeline and its probability to close, but as disciplined as this ranking may be, it is not “analytics”—that is, it is not derived from the application of mathematics on data The fact the sales rep enters the “forecast” into the CRM does not transform it to anything beyond a guess.

While sales reps are often good guessers, they achieve many of their forecasts, especially at the end of a quarter, through a modicum of

“unnatural” acts that have deep discounts and concessions the business pays for in reduced profitability down the road.

Analytics provides unbiased intelligence that is an essential input into decisions, as the mathematics of analytics is dispassionate Formulas have no predisposition to a desired outcome Data about the past is historical As such, the combination of math and history yields a view to what the future can be vs what one wants the future to be.

Business needs human intuition, as we have a good sense of what is around

us, but we are biased about what is ahead of us As such, when looking

forward, there is a fundamental need to incorporate unbiased predictions and forecasts that can be gained from analytics When the two are

combined, the man-and-machine efforts produce higher accuracy predictions over a longer time horizon.

AI AND ROI

Research typically pegs the ROI on analytics at a minimum of 10X For example, according to Boston-based NUCLEUS Research, the 2014 survey on Analytics ROI revealed that the average return “has increased to $13.01 for every Analytics dollar invested.”5 An excerpt from a November 2011 Research Note from NUCLEUS Research highlights the visibility that analytics provides:6

Software buyers may think that vendors overhype visibility as a benefit of analytics, but Nucleus found that, in fact, the highest-ROI analytics deployments made data more available to decision makers and enabled them to find ways to increase revenues or reduce costs Nucleus found analytics enabled improved visibility in three areas:

Revenues The more managers knew about what customers where (sic) buying and why, the better able they were to accelerate sales cycles, cross sell, and maximize pricing.

Gross margin By serving up highly granular data on costs of goods sold, analytics applications helped decision makers identify the highest margin products so that they could push the right products and increase gross profit.

Expenses The more managers … learned [from] analytics … the better able they were to reduce or eliminate expenditures that were unnecessary or generated low returns.

As seen in Figure 1.3, the report “The Analytics Advantage, We're just getting started,” from Deloitte, reflects key findings from the Deloitte Analytics Advantage Survey, including “Nearly half of all respondents (49 percent) assert that the greatest benefit of using analytics is that it is a key factor in better decision-making capabilities.” Further, when asked “Does analytics improve competitive positioning?” some 55% of respondents indicated that analytics Fairly to Significantly improved positioning.7

Figure 1.3 Deloitte analytics for decision-making.

With executives agreeing on the value of analytics for decisions and competitive capability, we note that business performance betterment projects must be measurable, and AI is no exception To this end, we believe that all analytics projects should start with a proof-of-concept or pilot to ensure that the quantification of benefits are measured, material, and achievable.

For example, at a data science conference, many speakers crowed about their projects with AI and analytics But what was notably absent in most of the presentations was a slide on ROI In one session, a member of the

audience specifically asked about ROI In a proud fashion, the presenting data scientist said the project saved enough money to hire another data scientist! Self-perpetuation is not ROI, and this example highlights the need to benchmark AI's contribution to business performance.

CONCLUSION

We live in an exciting time for change Much has been done by business to advance productivity, and with it, people's lives For example, at the turn of the twentieth century, the invention of electric power and the electric motor fundamentally and dramatically changed society, with immense benefits for mankind Even more than the electric motor's introduction, AI will make profound changes over the next generation and beyond.

Essentially, all businesses today realize that AI and analytics must be incorporated Some know what AI-enabled analytics is; but, unfortunately, only a few know how to incorporate AI, and then only on a limited basis The goal of this book is to empower all leaders with vision and clarity about

how to implement a culture of analytics for data-driven decisions and to

provide a Roadmap to get there In the next chapter, we discuss why AI and analytics need to be part of business, regardless of size 3 Zwerling, R.J and Sorensen, J.H (2019) AI & ML basics in business.

Finance Analytics Institute, Analytics Academy.

4 Zwerling, R.J and Sorensen, J.H (2019) Visualization vs analytics, what each tool is, how they are different & where they apply Finance Analytics Institute, Analytics Academy.

5 McDonald, A (2015) Analytics ROI—how to measure and maximize the value of analytics? Eckerson Group.

https://www.eckerson.com/articles/analytics-roi-how-to-measure-and-maximize-the-value-of-analytics.

6 Nucleus Research (2011) Analytics pays back $10.66 for every dollar spent Research Note

https://www.ironsidegroup.com/wp- content/uploads/2012/06/l122-Analytics-pays-back-10.66-for-every-dollar-spent.pdf.

7 Deloitte (2013) The analytics advantage: we're just getting started dttl-analytics-analytics-advantage-report-061913.pdf (deloitte.com).

We are at the dawn of an era of digitization for business that requires on-demand continuous planning where human and artificial intelligence (AI) work hand in hand to achieve insights for better results from data-driven decisions New software and cloud computing are making analytics more available, but is business adopting these new tools at a break-neck pace? Well, no! The adage “You can lead a horse to water, but can’t make him drink” is sadly applicable to many business experiments in analytics that have proven successful—but all too often are not sustained.

We will explore in subsequent chapters the impediments to analytics, but here our attention turns to why analytics is essential for business and why the executive must embrace the implementation of AI and analytics.

First, without analytics, the business cannot remain competitive and will be at risk of making decisions that fail to recognize market opportunities, ineffectively deploy capital, and misallocate staff resources to low-value efforts Second, without analytics-based decisions, we as humans will continue to be inherently biased, which leads to under-optimized

performance Third, executives pursuing analytics have a better chance of being rewarded from improved business performance; those who do not risk being passed over Accordingly, we will dive into the competitiveness, decision processes, and career advancement that analytics supports.

Today’s competitive landscape requires the adoption of analytics for

business to remain competitive, growing, and profitable The business that can plan better, wins! For example, if Company A can more accurately

forecast its demand, then it gains efficiency over costs and use of capital to better allocate to grow its markets; whereas Company B, which has failed to better forecast demand, loses market share due to the inability to fulfill demand or inefficiency in its costs that leads to higher prices.

This example seems obvious, yet the stampede to incorporate AI-enabled analytics in business is slow to develop, often from the lack of people skills and analytics tools, but primarily from an executive’s perspective to under-value the benefits from AI Until executives understand and believe in the value from AI, business will confront massive amounts of data with

spreadsheets, which is akin to taking a cross-country trip on a tricycle Fine if you have the time—but you don’t.

Unfortunately, too many executives do not appreciate or understand the value of AI and analytics to solve business problems, such as optimizing areas of the business and actions that can be derived from insights to improve the business This is due to several factors, including lack of

executive training on analytics, no advocate emerging to make a compelling case for analytics, and, as is often true with other innovations, executives who are risk-averse about investing in what they do not understand or accepting a risk of failure.

The lessons learned from prior business technology revolutions have taught that the need to enter the modern digital transformation era is a requirement and not an option In times past, businesses that have not evolved with the changes have perished or, worse, become insignificant players in their industry segment.

Think IBM, once the preeminent and dominant name in computers in the twentieth century, is little spoken of in the 2020s Still a $70 billion

company, IBM is not a point of presence in Silicon Valley, which has bred competitors to take mindshare when it comes to computer innovation and relevance Think too of Kodak, born in the 1880s, a onetime “blue-chip” company that held 90% of the film and 85% of the camera market in the United States As late as 1996, it was a $16 billion company with 145,000 employees But the switch to digital cameras and smartphones decimated Kodak, and by 2012, Kodak filed for bankruptcy protection.2

In comparing the 1955 Fortune 500 list of companies to the 2019 list, there remain only 52 companies The penalty for not recognizing the emerging

digital transformation era will be just as severe Companies like Blackberry, Nokia, and Motorola are shadows of the prevailing players they once were in the market they shaped Conversely, companies like Amazon and Netflix have led the way and dominated with AI and analytics Note, though, that adverse consequences are not limited to large companies and are equally applicable to companies of any size or industry, and public, private, profit, or non-profit.

The executive who does not realize the value from analytics or fails to adopt will be replaced by an executive who can deliver insights for data-driven decisions This is inevitable because executives who fail to do so will endanger their company’s performance and competitive position.

HUMAN JUDGMENT AND DECISION-MAKING

In business, human decision-making does not always optimize performance because it is vulnerable to bias and intuition: that is, gut feel We are

naturally intuitive about the future but quantitatively limited to calculate what the future probably can be We react to events and rely on experience to “guide” us to a decision We also may have a personal want or need that influences and impacts our decisions.

As such, we must first understand how nature has wired us to make

decisions before we can appreciate and accept how analytics can contribute to enhancing decision-making that can lead to improved business

performance The need to balance our instinctive judgment with AI for decisions is necessary to fulfill the potential value of analytics in business and avoid the shortcomings associated with traditional decision-making The research of Kahneman and Tversky, who received the Nobel Prize for Economics in 2002, produced a ground-breaking understanding of human judgment and decision-making under uncertainty Their research is viewed as one of the most influential social science behavioral insights of the past century It challenged the notion held by many economists that the human mind is unconsciously rational.

Kahneman authored a book, Thinking, Fast and Slow; the central thesis isthe interplay between what he terms System 1 and System 2 thinking.3 In System 1, a person has an instinctual response that is automatic and rapid

and has been shaped by experience and expertise For example, how much is 2 plus 2? Hopefully, you said 4 Your response was immediate and almost instinctive because, over many years, this simple answer has always been the same In effect, System 1 seeks coherence and applies relevant

memories to explain events or make decisions.

System 2 is invoked for more complex, thoughtful reasoning and is

characterized by slower, more rational analysis but is “prone to laziness and fatigue.” If you want to conduct your own experiment along these lines, ask someone to write down the results of a hypothetical sequence of 20 coin flips Then ask the person to flip a coin 20 times and write down the results The actual flips will almost certainly contain streaks of only heads or tails —the sorts of streaks that people do not think a random coin produces on its own This kind of misconception leads us to incorrectly analyze all sorts of situations in business, politics, and everyday life.

Further, the research of Kahneman and Tversky revealed previously undiscovered patterns of human irrationality: the ways that our minds consistently fool us and the steps we can take, at least some of the time, to

avoid being fooled They used the word heuristics to describe the rules of

thumb that often lead people astray.

One such rule is the halo effect, in which thinking about one positive

attribute of a person or thing causes observers to perceive other strengths that are not actually there For example, a project team was discussing the status of a new marketing campaign The campaign was led by Billy, who had a reputation for delivering successful campaigns Team members were asked to give their assessment of progress and, recognizing Billy’s past successes, gave positive evaluations This reflected the halo effect in that the past successes extended to this project without any factual basis other than Billy’s reputation.

This work has led to advances in individual behavior It is full of practical little ideas like “No one ever made a decision because of a number”; Kahneman has said, “They need a story.” Or Tversky’s theory of

socializing: because stinginess and generosity are both contagious, and because behaving generously makes you happier, surround yourself with generous people.

The research has clarified how decisions are made and underlying

influences that can impact decisions These influences are inherent in group interactions and individual biases, which are key to understanding the

balance between human judgment and analytics in decision-making.

Group Decision-Making

Several recognized behavioral group decision-making processes occur in forms that are considered flawed because they contain bias They lack the tools of analytics to inject unbiased insights into the decision process One

of these occurrences is often referred to as the Abilene paradox, where a

group of people collectively decide on a course of action that is counter to the preferences of many or all of the individuals in the group.4 It involves a common breakdown of group communication in which each member

mistakenly believes that their own preferences are counter to the group’s and, therefore, does not raise objections A common phrase relating to the Abilene paradox is a desire to “not rock the boat.”

For example, the design team of a successful smartphone is deciding whether to remove the home button on its next version release The lead designer suggests that the home button be kept, and the decision, after some discussion, is to keep the home button Later that day, some of the design team meet for lunch, and Peter expresses his preference for removing the home button Mickey jumps in to say “Me too!” and is followed by Davey They all acquiesced to the decision since they believed they were the only ones who did not agree In fact, when the team reassembled, most of the other members also preferred to remove the home button but also did not express their preference.

Another group decision-making process is groupthink, a mode of thinking

in which individual members of small cohesive groups tend to accept a decision that represents a perceived group consensus, whether or not the group members believe it to be valid, correct, or optimal.5 Groupthink reduces the efficiency of collective problem solving within such groups and perpetuates bias and flawed assumptions.

For example, a capital project review team is convened to decide on next year’s CapEx budget Each member is asked to indicate their preferred #1 project After the first and second members express their preference for the

same project, each succeeding member agrees with the first two, although their individual preferences were different They “go along to get along” and express their agreement with the preferred project.

Ronald Sims writes that the Abilene paradox is like groupthink but differs in significant ways, including that in groupthink, individuals are not acting contrary to their conscious wishes and generally feel good about the

decisions the group has reached.6 According to Sims, in the Abilene

paradox, the individuals acting contrary to their own wishes are more likely to have negative feelings about the outcome In Sims’ view, groupthink is a psychological phenomenon affecting clarity of thought, whereas in the Abilene paradox, thought is unaffected.

These group decision-making processes demonstrate the embedded flaws in human behavior that can produce decisions that lead to under-optimized, inefficient, ineffective, or non-competitive business performance that wastes capital and resources This punctuates why unbiased, scientific AI and analytics inputs are essential to minimize or eliminate group bias and contribute to improved business performance.

Individual Bias in Decision-Making

Individuals think in System 1 (thinking fast), which is the intuitive, “gut reaction” for making decisions System 2 (thinking slow) is the analytical, “critical thinking” way of making decisions Most of us identify with

System 2 thinking We consider ourselves rational, analytical beings Thus, we believe we spend most of our time engaged in System 2 thinking.

Actually, we spend almost all of our decision-making engaged in System 1 Only if we encounter something unexpected, or if we make a conscious effort, do we engage System 2.

System 1 thinking produces various forms of bias; several of the critical modes of bias more recognized by behavioral psychologists are discussed next:

Inherent bias: One of the biggest problems with System 1 is that it

seeks to quickly create a coherent, plausible story—an explanation for what is happening—by relying on associations and memories, pattern-matching, and assumptions The amount and quality of the data on

which the story is based are largely irrelevant System 1 will default to a plausible, convenient story even if that story is based on incorrect information.

For example, suppose a customer who usually orders a certain product places an order for an amount considerably less than expected Management assumes the customer’s business is down, when, in fact, the competitor has captured the customer’s business In effect, management has rationalized the event rather than seeking objective information on the cause.

Hindsight bias: People will reconstruct a story around past events to

underestimate the extent to which they were surprised by those future events This is an “I knew it all along” bias If an event comes to pass, people exaggerate the probability that they knew it was going to occur If an event does not occur, people erroneously recall that they thought it was unlikely In either case, these interpretations were based on subjective (biased) use of data.

For example, revenue forecasts received from marketing indicated that product sales would grow even though last month’s sales were below budget However, actual sales were materially below the forecast, upon which the executive says, “I knew it all along” in hindsight, but that concern was not acted upon when the forecast was accepted.

Confirmation bias: People will be quick to seize on limited evidence

that confirms their existing perspective And they will ignore or fail to seek evidence that runs contrary to the coherent story they have

already created in their mind.

For example, imagine a business considering launching a new

product The CEO has an idea for the “next big thing” and directs the team to conduct market research The team launches surveys, focus groups, and competitive analysis However, to satisfy the CEO, the team seeks to confirm the idea, only accepting evidence to support the feasibility of the product and disregarding contradictory information.

Noise bias: According to Kahneman and Sibony,7 noise is the

variability when making judgments that go in different directions For

example, a company is building a new plant to manufacture its

recently approved flu medicine The plant is scheduled to be online in six months The project team was asked to estimate (judge) when the first shipment could be expected, and the estimates ranged from 3 months ahead of schedule to 12 months behind schedule This variability is the noise in judgment and significantly influences the decisions that follow and the operating impacts affected by these judgments.

These examples illustrate the risks inherent in individual biases that can steer decision-making in the wrong direction They also demonstrate the need for unbiased AI-enabled analytics input to be a powerful

counterbalance to make more effective decisions that improve business performance.

As humans, we cannot avoid our natural instinct that drives us to System 1 thinking for most of our daily lives It is important for us to recognize when we are relying on it incorrectly for decision-making and the need to force System 2 thinking that incorporates AI and analytics as the preferred way to arrive at important business decisions and actions.

The era of human judgment for decision-making needs to evolve into a process that is more objective, insightful, and unbiased AI-enabled analytics is the vehicle to introduce in the decision process to support or contradict human bias As such, organizations must adopt an Analytics Culture that values the need for data-driven decisions as essential to assure and improve business performance.

1 The actual quote is, “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful”: George E.P.

Box Draper, N.R (2007) Response Surfaces, Mixtures, and RidgeAnalyses, 63 John Wiley & Sons.